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danet.yml
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Collections:
- Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Name: danet
Models:
- Config: configs/danet/danet_r50-d8_512x1024_40k_cityscapes.py
In Collection: danet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 375.94
lr schd: 40000
memory (GB): 7.4
Name: danet_r50-d8_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.74
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_40k_cityscapes/danet_r50-d8_512x1024_40k_cityscapes_20200605_191324-c0dbfa5f.pth
- Config: configs/danet/danet_r101-d8_512x1024_40k_cityscapes.py
In Collection: danet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,1024)
value: 502.51
lr schd: 40000
memory (GB): 10.9
Name: danet_r101-d8_512x1024_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 80.52
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_40k_cityscapes/danet_r101-d8_512x1024_40k_cityscapes_20200605_200831-c57a7157.pth
- Config: configs/danet/danet_r50-d8_769x769_40k_cityscapes.py
In Collection: danet
Metadata:
backbone: R-50-D8
crop size: (769,769)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 641.03
lr schd: 40000
memory (GB): 8.8
Name: danet_r50-d8_769x769_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 78.88
mIoU(ms+flip): 80.62
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_40k_cityscapes/danet_r50-d8_769x769_40k_cityscapes_20200530_025703-76681c60.pth
- Config: configs/danet/danet_r101-d8_769x769_40k_cityscapes.py
In Collection: danet
Metadata:
backbone: R-101-D8
crop size: (769,769)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (769,769)
value: 934.58
lr schd: 40000
memory (GB): 12.8
Name: danet_r101-d8_769x769_40k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.88
mIoU(ms+flip): 81.47
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_40k_cityscapes/danet_r101-d8_769x769_40k_cityscapes_20200530_025717-dcb7fd4e.pth
- Config: configs/danet/danet_r50-d8_512x1024_80k_cityscapes.py
In Collection: danet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Name: danet_r50-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.34
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x1024_80k_cityscapes/danet_r50-d8_512x1024_80k_cityscapes_20200607_133029-2bfa2293.pth
- Config: configs/danet/danet_r101-d8_512x1024_80k_cityscapes.py
In Collection: danet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Name: danet_r101-d8_512x1024_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 80.41
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x1024_80k_cityscapes/danet_r101-d8_512x1024_80k_cityscapes_20200607_132918-955e6350.pth
- Config: configs/danet/danet_r50-d8_769x769_80k_cityscapes.py
In Collection: danet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Name: danet_r50-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 79.27
mIoU(ms+flip): 80.96
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_769x769_80k_cityscapes/danet_r50-d8_769x769_80k_cityscapes_20200607_132954-495689b4.pth
- Config: configs/danet/danet_r101-d8_769x769_80k_cityscapes.py
In Collection: danet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Name: danet_r101-d8_769x769_80k_cityscapes
Results:
Dataset: Cityscapes
Metrics:
mIoU: 80.47
mIoU(ms+flip): 82.02
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_769x769_80k_cityscapes/danet_r101-d8_769x769_80k_cityscapes_20200607_132918-f3a929e7.pth
- Config: configs/danet/danet_r50-d8_512x512_80k_ade20k.py
In Collection: danet
Metadata:
backbone: R-50-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 47.17
lr schd: 80000
memory (GB): 11.5
Name: danet_r50-d8_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 41.66
mIoU(ms+flip): 42.9
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_80k_ade20k/danet_r50-d8_512x512_80k_ade20k_20200615_015125-edb18e08.pth
- Config: configs/danet/danet_r101-d8_512x512_80k_ade20k.py
In Collection: danet
Metadata:
backbone: R-101-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 70.52
lr schd: 80000
memory (GB): 15.0
Name: danet_r101-d8_512x512_80k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 43.64
mIoU(ms+flip): 45.19
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_80k_ade20k/danet_r101-d8_512x512_80k_ade20k_20200615_015126-d0357c73.pth
- Config: configs/danet/danet_r50-d8_512x512_160k_ade20k.py
In Collection: danet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Name: danet_r50-d8_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 42.45
mIoU(ms+flip): 43.25
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_160k_ade20k/danet_r50-d8_512x512_160k_ade20k_20200616_082340-9cb35dcd.pth
- Config: configs/danet/danet_r101-d8_512x512_160k_ade20k.py
In Collection: danet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Name: danet_r101-d8_512x512_160k_ade20k
Results:
Dataset: ADE20K
Metrics:
mIoU: 44.17
mIoU(ms+flip): 45.02
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_160k_ade20k/danet_r101-d8_512x512_160k_ade20k_20200616_082348-23bf12f9.pth
- Config: configs/danet/danet_r50-d8_512x512_20k_voc12aug.py
In Collection: danet
Metadata:
backbone: R-50-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 47.76
lr schd: 20000
memory (GB): 6.5
Name: danet_r50-d8_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 74.45
mIoU(ms+flip): 75.69
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_20k_voc12aug/danet_r50-d8_512x512_20k_voc12aug_20200618_070026-9e9e3ab3.pth
- Config: configs/danet/danet_r101-d8_512x512_20k_voc12aug.py
In Collection: danet
Metadata:
backbone: R-101-D8
crop size: (512,512)
inference time (ms/im):
- backend: PyTorch
batch size: 1
hardware: V100
mode: FP32
resolution: (512,512)
value: 72.67
lr schd: 20000
memory (GB): 9.9
Name: danet_r101-d8_512x512_20k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.02
mIoU(ms+flip): 77.23
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_20k_voc12aug/danet_r101-d8_512x512_20k_voc12aug_20200618_070026-d48d23b2.pth
- Config: configs/danet/danet_r50-d8_512x512_40k_voc12aug.py
In Collection: danet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
Name: danet_r50-d8_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.37
mIoU(ms+flip): 77.29
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r50-d8_512x512_40k_voc12aug/danet_r50-d8_512x512_40k_voc12aug_20200613_235526-426e3a64.pth
- Config: configs/danet/danet_r101-d8_512x512_40k_voc12aug.py
In Collection: danet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
Name: danet_r101-d8_512x512_40k_voc12aug
Results:
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.51
mIoU(ms+flip): 77.32
Task: Semantic Segmentation
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/danet/danet_r101-d8_512x512_40k_voc12aug/danet_r101-d8_512x512_40k_voc12aug_20200613_223031-788e232a.pth